Preparation of the data

In [1]:
In [2]:
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/images/Cars72.png
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/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars50.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars60.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars315.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars14.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars207.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars224.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars225.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars333.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars122.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars82.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars419.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars173.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars84.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars184.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars344.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars356.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars25.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars48.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars68.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars351.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars367.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars305.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars108.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars148.xml
/home/aarsh/Jupyter Notebooks/Licence Plate Detection/Input/annotations/Cars170.xml

We create the variable X containing all the images of cars by resizing them.

In [3]:
In [4]:

We create the variable y containing all the bounding boxe annotations (label). Before that, we will have to resize the annotations so that it fits the new size of the images (200*200). We create a function resizeannotation for that.

In [5]:
In [6]:
In [7]:
Out[7]:
[188, 141, 101, 104]
In [8]:
Out[8]:
[187, 144, 101, 104]

We check X and y shape

In [9]:
Out[9]:
(433, 224, 224, 3)
In [10]:
Out[10]:
(433, 4)

And we display the first twenty image of the dataset :

In [11]:

We can draw the rectangle containing the license plate using the OpenCV library

In [12]:
In [13]:

Preparing for CNN

In [14]:
In [15]:

We split our dataset in two : training set/testing set

In [16]:

Convolutionnal Neural Network

In [17]:

Create the model

In [18]:
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
vgg16 (Functional)           (None, 7, 7, 512)         14714688  
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
dense (Dense)                (None, 128)               3211392   
_________________________________________________________________
dense_1 (Dense)              (None, 128)               16512     
_________________________________________________________________
dense_2 (Dense)              (None, 64)                8256      
_________________________________________________________________
dense_3 (Dense)              (None, 4)                 260       
=================================================================
Total params: 17,951,108
Trainable params: 3,236,420
Non-trainable params: 14,714,688
_________________________________________________________________
In [19]:
In [20]:
Epoch 1/50
10/10 [==============================] - 38s 4s/step - loss: 0.0606 - accuracy: 0.3642 - val_loss: 0.0209 - val_accuracy: 0.6571
Epoch 2/50
10/10 [==============================] - 38s 4s/step - loss: 0.0172 - accuracy: 0.6410 - val_loss: 0.0179 - val_accuracy: 0.6571
Epoch 3/50
10/10 [==============================] - 37s 4s/step - loss: 0.0109 - accuracy: 0.6322 - val_loss: 0.0160 - val_accuracy: 0.5429
Epoch 4/50
10/10 [==============================] - 36s 4s/step - loss: 0.0056 - accuracy: 0.7473 - val_loss: 0.0139 - val_accuracy: 0.8000
Epoch 5/50
10/10 [==============================] - 36s 4s/step - loss: 0.0032 - accuracy: 0.8134 - val_loss: 0.0149 - val_accuracy: 0.6000
Epoch 6/50
10/10 [==============================] - 36s 4s/step - loss: 0.0024 - accuracy: 0.8215 - val_loss: 0.0149 - val_accuracy: 0.6857
Epoch 7/50
10/10 [==============================] - 36s 4s/step - loss: 0.0013 - accuracy: 0.8487 - val_loss: 0.0149 - val_accuracy: 0.8000
Epoch 8/50
10/10 [==============================] - 36s 4s/step - loss: 8.8762e-04 - accuracy: 0.9313 - val_loss: 0.0151 - val_accuracy: 0.7143
Epoch 9/50
10/10 [==============================] - 36s 4s/step - loss: 7.3836e-04 - accuracy: 0.9127 - val_loss: 0.0150 - val_accuracy: 0.7714
Epoch 10/50
10/10 [==============================] - 36s 4s/step - loss: 5.0768e-04 - accuracy: 0.9525 - val_loss: 0.0150 - val_accuracy: 0.7714
Epoch 11/50
10/10 [==============================] - 36s 4s/step - loss: 2.9462e-04 - accuracy: 0.9584 - val_loss: 0.0152 - val_accuracy: 0.7714
Epoch 12/50
10/10 [==============================] - 36s 4s/step - loss: 4.8063e-04 - accuracy: 0.9495 - val_loss: 0.0151 - val_accuracy: 0.7714
Epoch 13/50
10/10 [==============================] - 36s 4s/step - loss: 4.9616e-04 - accuracy: 0.9365 - val_loss: 0.0153 - val_accuracy: 0.7714
Epoch 14/50
10/10 [==============================] - 36s 4s/step - loss: 5.4088e-04 - accuracy: 0.9740 - val_loss: 0.0153 - val_accuracy: 0.7714
Epoch 15/50
10/10 [==============================] - 36s 4s/step - loss: 4.2817e-04 - accuracy: 0.9266 - val_loss: 0.0150 - val_accuracy: 0.7714
Epoch 16/50
10/10 [==============================] - 36s 4s/step - loss: 4.0861e-04 - accuracy: 0.9697 - val_loss: 0.0156 - val_accuracy: 0.7714
Epoch 17/50
10/10 [==============================] - 36s 4s/step - loss: 3.3207e-04 - accuracy: 0.9734 - val_loss: 0.0149 - val_accuracy: 0.7714
Epoch 18/50
10/10 [==============================] - 36s 4s/step - loss: 4.4654e-04 - accuracy: 0.9805 - val_loss: 0.0151 - val_accuracy: 0.7714
Epoch 19/50
10/10 [==============================] - 36s 4s/step - loss: 1.8828e-04 - accuracy: 0.9841 - val_loss: 0.0154 - val_accuracy: 0.8000
Epoch 20/50
10/10 [==============================] - 36s 4s/step - loss: 2.4736e-04 - accuracy: 0.9576 - val_loss: 0.0149 - val_accuracy: 0.8286
Epoch 21/50
10/10 [==============================] - 36s 4s/step - loss: 2.0369e-04 - accuracy: 0.9620 - val_loss: 0.0154 - val_accuracy: 0.7429
Epoch 22/50
10/10 [==============================] - 36s 4s/step - loss: 3.5842e-04 - accuracy: 0.9541 - val_loss: 0.0151 - val_accuracy: 0.7714
Epoch 23/50
10/10 [==============================] - 36s 4s/step - loss: 4.2823e-04 - accuracy: 0.9669 - val_loss: 0.0148 - val_accuracy: 0.7714
Epoch 24/50
10/10 [==============================] - 36s 4s/step - loss: 2.4954e-04 - accuracy: 0.9590 - val_loss: 0.0150 - val_accuracy: 0.7714
Epoch 25/50
10/10 [==============================] - 36s 4s/step - loss: 5.0298e-04 - accuracy: 0.9474 - val_loss: 0.0150 - val_accuracy: 0.7714
Epoch 26/50
10/10 [==============================] - 36s 4s/step - loss: 3.1339e-04 - accuracy: 0.9817 - val_loss: 0.0151 - val_accuracy: 0.7429
Epoch 27/50
10/10 [==============================] - 36s 4s/step - loss: 3.3250e-04 - accuracy: 0.9678 - val_loss: 0.0154 - val_accuracy: 0.7714
Epoch 28/50
10/10 [==============================] - 36s 4s/step - loss: 2.8291e-04 - accuracy: 0.9586 - val_loss: 0.0148 - val_accuracy: 0.6857
Epoch 29/50
10/10 [==============================] - 36s 4s/step - loss: 2.3252e-04 - accuracy: 0.9519 - val_loss: 0.0155 - val_accuracy: 0.7714
Epoch 30/50
10/10 [==============================] - 36s 4s/step - loss: 2.5726e-04 - accuracy: 0.9765 - val_loss: 0.0150 - val_accuracy: 0.7714
Epoch 31/50
10/10 [==============================] - 36s 4s/step - loss: 5.4040e-04 - accuracy: 0.9697 - val_loss: 0.0155 - val_accuracy: 0.7714
Epoch 32/50
10/10 [==============================] - 36s 4s/step - loss: 2.9837e-04 - accuracy: 0.9799 - val_loss: 0.0147 - val_accuracy: 0.7714
Epoch 33/50
10/10 [==============================] - 36s 4s/step - loss: 5.8122e-04 - accuracy: 0.9403 - val_loss: 0.0150 - val_accuracy: 0.8286
Epoch 34/50
10/10 [==============================] - 36s 4s/step - loss: 3.3812e-04 - accuracy: 0.9476 - val_loss: 0.0155 - val_accuracy: 0.7714
Epoch 35/50
10/10 [==============================] - 36s 4s/step - loss: 4.7334e-04 - accuracy: 0.9592 - val_loss: 0.0154 - val_accuracy: 0.6857
Epoch 36/50
10/10 [==============================] - 36s 4s/step - loss: 4.7175e-04 - accuracy: 0.9600 - val_loss: 0.0151 - val_accuracy: 0.7714
Epoch 37/50
10/10 [==============================] - 36s 4s/step - loss: 4.5039e-04 - accuracy: 0.9414 - val_loss: 0.0155 - val_accuracy: 0.7714
Epoch 38/50
10/10 [==============================] - 36s 4s/step - loss: 7.1393e-04 - accuracy: 0.9569 - val_loss: 0.0150 - val_accuracy: 0.8000
Epoch 39/50
10/10 [==============================] - 36s 4s/step - loss: 3.1769e-04 - accuracy: 0.9601 - val_loss: 0.0153 - val_accuracy: 0.8000
Epoch 40/50
10/10 [==============================] - 36s 4s/step - loss: 3.8842e-04 - accuracy: 0.9384 - val_loss: 0.0152 - val_accuracy: 0.7429
Epoch 41/50
10/10 [==============================] - 36s 4s/step - loss: 8.7367e-04 - accuracy: 0.9596 - val_loss: 0.0160 - val_accuracy: 0.8000
Epoch 42/50
10/10 [==============================] - 36s 4s/step - loss: 5.4115e-04 - accuracy: 0.9468 - val_loss: 0.0152 - val_accuracy: 0.7714
Epoch 43/50
10/10 [==============================] - 36s 4s/step - loss: 4.3775e-04 - accuracy: 0.9620 - val_loss: 0.0147 - val_accuracy: 0.8000
Epoch 44/50
10/10 [==============================] - 36s 4s/step - loss: 3.9895e-04 - accuracy: 0.9897 - val_loss: 0.0149 - val_accuracy: 0.7429
Epoch 45/50
10/10 [==============================] - 36s 4s/step - loss: 4.8696e-04 - accuracy: 0.9650 - val_loss: 0.0153 - val_accuracy: 0.7429
Epoch 46/50
10/10 [==============================] - 36s 4s/step - loss: 4.7146e-04 - accuracy: 0.9585 - val_loss: 0.0153 - val_accuracy: 0.7143
Epoch 47/50
10/10 [==============================] - 36s 4s/step - loss: 4.4948e-04 - accuracy: 0.9624 - val_loss: 0.0150 - val_accuracy: 0.8000
Epoch 48/50
10/10 [==============================] - 36s 4s/step - loss: 4.4114e-04 - accuracy: 0.9504 - val_loss: 0.0146 - val_accuracy: 0.7714
Epoch 49/50
10/10 [==============================] - 36s 4s/step - loss: 6.3404e-04 - accuracy: 0.9306 - val_loss: 0.0157 - val_accuracy: 0.7714
Epoch 50/50
10/10 [==============================] - 36s 4s/step - loss: 6.1543e-04 - accuracy: 0.9492 - val_loss: 0.0149 - val_accuracy: 0.7714
In [21]:
In [22]:
Score : 83.91%
In [23]:
In [24]:
In [25]:
 87/100 [=========================>....] - ETA: 1s - loss: 0.0075 - accuracy: 0.8391WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 100 batches). You may need to use the repeat() function when building your dataset.
100/100 [==============================] - 9s 93ms/step - loss: 0.0075 - accuracy: 0.8391
Test results 
 Loss: 0.007483365945518017 
 Accuracy 0.8390804529190063
In [56]:
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We can see how our model localize license plates on our testing set :

In [59]:
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Character recognition in licence plate

In [75]:

Read Input Image

In [88]:

Convert Image to Grayscale

In [89]:

Maximize Contrast

In [90]:

Adaptive Thresholding

In [91]:

Finding Contours to locate plate

In [92]:

Data Preparation

In [93]:
In [94]:
In [95]:

Imposing Boxes on Original Image of Car

Drawing Boxes on the characters of license plate(To see progress)

In [96]:
In [97]:

Thresholding Again to Find Chars

Making the Chars more distinctive

In [98]:

Taking Negative Again

In [99]:
In [100]:

A function to seperate the characters

Find characters in the resulting images

In [101]:
In [102]:

Separating the Characters in the Plate

Separating the Characters in the Plate to identify them seperatly and using the model definied later for it

In [103]:
In [105]:

Training and Validation Data Processing for character recognition

In [152]:
Found 864 images belonging to 36 classes.
Found 216 images belonging to 36 classes.
In [153]:

Model for Training

In [154]:
In [155]:
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 28, 28, 16)        23248     
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 32)        131104    
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        131136    
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 128)       131200    
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dropout (Dropout)            (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               65664     
_________________________________________________________________
dense_2 (Dense)              (None, 36)                4644      
=================================================================
Total params: 3,698,772
Trainable params: 3,698,772
Non-trainable params: 0
_________________________________________________________________

Making a Condition for Ending the Training Quickly once the required accuracy is achieved

In [156]:

Training

In [157]:
Epoch 1/80
864/864 [==============================] - 37s 42ms/step - loss: 3.5372 - custom_f1score: 0.0520 - val_loss: 2.4102 - val_custom_f1score: 0.3036
Epoch 2/80
864/864 [==============================] - 37s 42ms/step - loss: 2.1106 - custom_f1score: 0.4032 - val_loss: 0.9329 - val_custom_f1score: 0.7664
Epoch 3/80
864/864 [==============================] - 36s 42ms/step - loss: 0.9673 - custom_f1score: 0.6877 - val_loss: 0.2854 - val_custom_f1score: 0.9226
Epoch 4/80
864/864 [==============================] - 36s 42ms/step - loss: 0.4954 - custom_f1score: 0.8286 - val_loss: 0.3125 - val_custom_f1score: 0.8899
Epoch 5/80
864/864 [==============================] - 37s 42ms/step - loss: 0.3431 - custom_f1score: 0.9013 - val_loss: 0.2016 - val_custom_f1score: 0.9315
Epoch 6/80
864/864 [==============================] - 37s 42ms/step - loss: 0.2848 - custom_f1score: 0.9108 - val_loss: 0.1297 - val_custom_f1score: 0.9598
Epoch 7/80
864/864 [==============================] - 37s 42ms/step - loss: 0.1917 - custom_f1score: 0.9321 - val_loss: 0.1414 - val_custom_f1score: 0.9643
Epoch 8/80
864/864 [==============================] - 37s 43ms/step - loss: 0.1580 - custom_f1score: 0.9449 - val_loss: 0.0789 - val_custom_f1score: 0.9717
Epoch 9/80
864/864 [==============================] - 37s 43ms/step - loss: 0.1368 - custom_f1score: 0.9496 - val_loss: 0.1635 - val_custom_f1score: 0.9375
Epoch 10/80
864/864 [==============================] - 37s 43ms/step - loss: 0.2565 - custom_f1score: 0.9330 - val_loss: 0.2516 - val_custom_f1score: 0.9226
Epoch 11/80
864/864 [==============================] - 37s 43ms/step - loss: 0.1180 - custom_f1score: 0.9550 - val_loss: 0.1167 - val_custom_f1score: 0.9554
Epoch 12/80
864/864 [==============================] - 37s 43ms/step - loss: 0.0905 - custom_f1score: 0.9628 - val_loss: 0.0546 - val_custom_f1score: 0.9777
Epoch 13/80
864/864 [==============================] - 37s 43ms/step - loss: 0.1062 - custom_f1score: 0.9691 - val_loss: 0.0927 - val_custom_f1score: 0.9732
Epoch 14/80
864/864 [==============================] - 37s 43ms/step - loss: 0.1517 - custom_f1score: 0.9498 - val_loss: 0.0423 - val_custom_f1score: 0.9896
Epoch 15/80
864/864 [==============================] - 37s 43ms/step - loss: 0.0935 - custom_f1score: 0.9639 - val_loss: 0.0761 - val_custom_f1score: 0.9643
Epoch 16/80
864/864 [==============================] - 38s 43ms/step - loss: 0.0787 - custom_f1score: 0.9647 - val_loss: 0.0942 - val_custom_f1score: 0.9628
Epoch 17/80
864/864 [==============================] - 38s 43ms/step - loss: 0.1178 - custom_f1score: 0.9640 - val_loss: 0.0521 - val_custom_f1score: 0.9851
Epoch 18/80
864/864 [==============================] - 38s 43ms/step - loss: 0.0707 - custom_f1score: 0.9741 - val_loss: 0.1742 - val_custom_f1score: 0.9821
Epoch 19/80
864/864 [==============================] - 37s 43ms/step - loss: 0.0967 - custom_f1score: 0.9784 - val_loss: 0.0549 - val_custom_f1score: 0.9866
Epoch 20/80
864/864 [==============================] - 37s 43ms/step - loss: 0.1179 - custom_f1score: 0.9592 - val_loss: 0.0507 - val_custom_f1score: 0.9821
Epoch 21/80
864/864 [==============================] - 38s 43ms/step - loss: 0.0595 - custom_f1score: 0.9667 - val_loss: 0.0650 - val_custom_f1score: 0.9762
Epoch 22/80
864/864 [==============================] - 38s 43ms/step - loss: 0.1490 - custom_f1score: 0.9599 - val_loss: 0.0391 - val_custom_f1score: 0.9896
Epoch 23/80
864/864 [==============================] - 38s 43ms/step - loss: 0.0667 - custom_f1score: 0.9743 - val_loss: 0.0816 - val_custom_f1score: 0.9717
Epoch 24/80
864/864 [==============================] - 38s 44ms/step - loss: 0.0667 - custom_f1score: 0.9866 - val_loss: 0.0542 - val_custom_f1score: 0.9688
Epoch 25/80
864/864 [==============================] - 38s 44ms/step - loss: 0.0521 - custom_f1score: 0.9733 - val_loss: 0.1817 - val_custom_f1score: 0.9583
Epoch 26/80
864/864 [==============================] - 38s 44ms/step - loss: 0.1002 - custom_f1score: 0.9625 - val_loss: 0.0723 - val_custom_f1score: 0.9732
Epoch 27/80
864/864 [==============================] - 38s 44ms/step - loss: 0.0562 - custom_f1score: 0.9667 - val_loss: 0.0663 - val_custom_f1score: 0.9807
Epoch 28/80
864/864 [==============================] - 38s 44ms/step - loss: 0.0907 - custom_f1score: 0.9702 - val_loss: 0.0531 - val_custom_f1score: 0.9807
Epoch 29/80
864/864 [==============================] - 38s 44ms/step - loss: 0.0638 - custom_f1score: 0.9810 - val_loss: 0.0207 - val_custom_f1score: 0.9911
Out[157]:
<tensorflow.python.keras.callbacks.History at 0x7f2790f18490>
In [167]:

Storing keras model

In [169]:
Saved model to disk

Loading the Pre-Trained Model(remove the Comments and run it)

In [171]:

Checking if the model is the same

In [172]:
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 28, 28, 16)        23248     
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 32)        131104    
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        131136    
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 128)       131200    
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dropout (Dropout)            (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               65664     
_________________________________________________________________
dense_2 (Dense)              (None, 36)                4644      
=================================================================
Total params: 3,698,772
Trainable params: 3,698,772
Non-trainable params: 0
_________________________________________________________________
None

Predicting the Output and Showcasing the Result

In [173]:
/home/aarsh/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '
PGMN112
In [174]:

THANK YOU